THE ETHICAL IMPLEMENTATION OF ALGORITHM-BASED DECISION-MAKING: AN ANALYTIC ROADMAP TO EXAMINE ACCOUNTABILITY AND RESPONSIBILITY OF DESIGNING AND IMPLEMENTING AUDIT DATA ANALYTICS

Abstract

This essay aims to manage the ethical issues of data and analytics in data-driven solutions. The proposed analytic roadmap emphasizes identifying potential root causes throughout the design and implementation process, from data collection to analytics output. The study adopts a futuristic approach to take proactive actions to manage the ethical issues at the beginning of the design. It concerns consequential ethics highly related to the analytical context. The interaction between emerging technology and the business context intensifies the uncertainty of data analytics’ consequences. The study theorizes eleven potential ethical dimensions from the technology level, artifact level, and application level. Then, a four-phase ethics assessment map is developed to evaluate and deal with these moral problems. The four stages include assessing the data source, training data selection, the algorithm’s traceability, and the output’s interpretation. The assessment guides a detailed discussion about handling “ethical laziness” to decrease the evidence’s entropy. One contribution of this study is that it offers a practical roadmap to solve the ethical issue in algorithmic accounting analytics, from raw data through the analytic output deployment.